Differential phospho/protein analysis - CRC Batch 1
Sophie Herbst
Analysis date: 2023-09-07
Depends on
DIPG_FirstBatch_DataProcessing Script
load("../Data/Cache/Xenografts_Batch1_2_DataProcessing.RData")TODO
- Do differential abudance analysis for prep batch and mass spec run
Setup
Load libraries and functions
Analysis
DEP
Tyrosine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set2, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pY")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.Return_DEP_Hits_Plots(data = pY_Set2_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(comparison)
##
## # Now:
## data %>% select(all_of(comparison))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## 'select()' returned 1:many mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.8348968
## 2: ABC transporter disorders 0.3705463
## 3: ABC-family proteins mediated transport 0.2386935
## 4: ADP signalling through P2Y purinoceptor 1 0.6960600
## 5: ALK mutants bind TKIs 0.3320388
## 6: APC/C-mediated degradation of cell cycle proteins 0.1331658
## padj log2err ES NES size leadingEdge
## 1: 0.9569891 0.05101141 0.4675325 0.7116487 2 6385,1464
## 2: 0.9198162 0.10632326 -0.5416351 -1.1266124 5 5696,5687,5692,5694
## 3: 0.8924453 0.14122512 -0.5585935 -1.2376386 6 5696,5687,1965,5692,5694
## 4: 0.9569891 0.05947603 0.5609045 0.8537737 2 1432,6714
## 5: 0.9198162 0.10063339 0.8341969 1.1265638 1 1213
## 6: 0.8321409 0.19381330 -0.6290186 -1.3936748 6 983,5696,5687data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.3238095
## 2: ABC transporter disorders 0.3942598
## 3: ABC-family proteins mediated transport 0.4770115
## 4: ADP signalling through P2Y purinoceptor 1 0.8547619
## 5: ALK mutants bind TKIs 0.6693227
## 6: APC/C-mediated degradation of cell cycle proteins 0.3318966
## padj log2err ES NES size leadingEdge
## 1: 0.7103992 0.11524000 0.6805195 1.1003725 2 1464,6385
## 2: 0.7171934 0.07687367 -0.5275245 -1.0495781 5 5692,5696,5693,5687
## 3: 0.7859918 0.06479434 -0.4748405 -1.0046256 6 5692,5696,5693,5687,1965
## 4: 0.9221326 0.06103637 0.4415584 0.7139821 2 1432,6714
## 5: 0.8590082 0.06421409 0.6658031 0.8945496 1 1213
## 6: 0.7103992 0.08336341 -0.5311741 -1.1238113 6 5692,983,5696,5693,5687Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.1926346
## 2: ABC transporter disorders 0.4444444
## 3: ABC-family proteins mediated transport 0.6400938
## 4: ADP signalling through P2Y purinoceptor 1 0.9214176
## 5: ALK mutants bind TKIs 0.2359551
## 6: APC/C-mediated degradation of cell cycle proteins 0.2532239
## padj log2err ES NES size leadingEdge
## 1: 0.5037070 0.16957064 0.7324675 1.2352115 2 1464
## 2: 0.6870382 0.05986031 -0.5053256 -1.0226331 5 5693,5696,5692
## 3: 0.8241230 0.04165568 -0.4223236 -0.8943592 6 5693,5696,5692
## 4: 0.9829994 0.03745842 -0.4311688 -0.6839154 2 6714,1432
## 5: 0.5422652 0.12043337 -0.8756477 -1.1854109 1 1213
## 6: 0.5604280 0.08705159 -0.5550656 -1.1754683 6 983,5693,5696,5692EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_Set2, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, additional_title = "pY", contrast = "EC_vs_E", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.5179063
## 2: ABC transporter disorders 0.4489796
## 3: ABC-family proteins mediated transport 0.5688406
## 4: ADP signalling through P2Y purinoceptor 1 0.7824726
## 5: ALK mutants bind TKIs 0.2914172
## 6: APC/C-mediated degradation of cell cycle proteins 0.7487923
## padj log2err ES NES size leadingEdge
## 1: 0.7907865 0.09497515 0.5714286 0.9433919 2 1464,6385
## 2: 0.7657428 0.06197627 -0.5443490 -1.0276232 5 5692,5693
## 3: 0.8342146 0.04840876 -0.4725743 -0.9392741 6 5692,5693
## 4: 0.9365650 0.04567904 -0.5220779 -0.8037836 2 6714,1432
## 5: 0.6904262 0.11056472 -0.8549223 -1.1480337 1 1213
## 6: 0.9212021 0.03576908 -0.4101428 -0.8151871 6 5692,5693
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set2, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.2434783
## 2: ABC transporter disorders 0.4963054
## 3: ABC-family proteins mediated transport 0.6213705
## 4: ADP signalling through P2Y purinoceptor 1 0.8158295
## 5: ALK mutants bind TKIs 0.1621622
## 6: APC/C-mediated degradation of cell cycle proteins 0.3275261
## padj log2err ES NES size leadingEdge
## 1: 0.7131233 0.15114876 0.7194805 1.1675565 2 1464,6385
## 2: 0.8009745 0.05559471 -0.5052912 -0.9897829 5 5693,5687,5696
## 3: 0.8826364 0.04258778 -0.4373559 -0.8979332 6 5693,5687,5696
## 4: 0.9335254 0.04250232 -0.5012987 -0.7823006 2 6714,1432
## 5: 0.5702233 0.15114876 -0.9222798 -1.2340474 1 1213
## 6: 0.8009745 0.07289386 -0.5410779 -1.1108843 6 5693,983,5687,5696## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Save
rowData(dep_ctrl_vs_E_pY) %>% as_tibble() %>% select(HGNC_Symbol, E_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch1_Set2_E_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, EC_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch1_Set2_EC_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.2 forcats_1.0.0
## [3] stringr_1.5.0 dplyr_1.1.2
## [5] purrr_1.0.2 readr_2.1.4
## [7] tidyr_1.3.0 tibble_3.2.1
## [9] ggplot2_3.4.2 tidyverse_2.0.0
## [11] mdatools_0.14.0 SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [15] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [17] DEP_1.20.0 org.Hs.eg.db_3.16.0
## [19] AnnotationDbi_1.60.2 IRanges_2.32.0
## [21] S4Vectors_0.36.2 Biobase_2.58.0
## [23] BiocGenerics_0.44.0 fgsea_1.24.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.15 fastmatch_1.1-3 plyr_1.8.8
## [4] igraph_1.5.1 gmm_1.8 lazyeval_0.2.2
## [7] shinydashboard_0.7.2 crosstalk_1.2.0 BiocParallel_1.32.6
## [10] digest_0.6.33 foreach_1.5.2 htmltools_0.5.6
## [13] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1
## [16] cluster_2.1.4 doParallel_1.0.17 tzdb_0.4.0
## [19] limma_3.54.2 ComplexHeatmap_2.14.0 Biostrings_2.66.0
## [22] imputeLCMD_2.1 sandwich_3.0-2 timechange_0.2.0
## [25] colorspace_2.1-0 blob_1.2.4 xfun_0.40
## [28] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.7
## [31] impute_1.72.3 zoo_1.8-12 iterators_1.0.14
## [34] glue_1.6.2 hash_2.2.6.2 gtable_0.3.3
## [37] zlibbioc_1.44.0 XVector_0.38.0 GetoptLong_1.0.5
## [40] DelayedArray_0.24.0 shape_1.4.6 scales_1.2.1
## [43] pheatmap_1.0.12 vsn_3.66.0 mvtnorm_1.2-2
## [46] DBI_1.1.3 Rcpp_1.0.11 plotrix_3.8-2
## [49] mzR_2.32.0 viridisLite_0.4.2 xtable_1.8-4
## [52] clue_0.3-64 reactome.db_1.82.0 bit_4.0.5
## [55] preprocessCore_1.60.2 sqldf_0.4-11 MsCoreUtils_1.10.0
## [58] DT_0.28 htmlwidgets_1.6.2 httr_1.4.6
## [61] gplots_3.1.3 RColorBrewer_1.1-3 ellipsis_0.3.2
## [64] farver_2.1.1 pkgconfig_2.0.3 XML_3.99-0.14
## [67] sass_0.4.7 utf8_1.2.3 STRINGdb_2.10.1
## [70] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
## [73] later_1.3.1 munsell_0.5.0 tools_4.2.3
## [76] cachem_1.0.8 cli_3.6.1 gsubfn_0.7
## [79] generics_0.1.3 RSQLite_2.3.1 fdrtool_1.2.17
## [82] evaluate_0.21 fastmap_1.1.1 mzID_1.36.0
## [85] yaml_2.3.7 knitr_1.43 bit64_4.0.5
## [88] caTools_1.18.2 KEGGREST_1.38.0 ncdf4_1.21
## [91] mime_0.12 compiler_4.2.3 rstudioapi_0.15.0
## [94] plotly_4.10.2 png_0.1-8 affyio_1.68.0
## [97] stringi_1.7.12 bslib_0.5.0 highr_0.10
## [100] MSnbase_2.24.2 lattice_0.21-8 ProtGenerics_1.30.0
## [103] Matrix_1.6-0 tmvtnorm_1.5 vctrs_0.6.3
## [106] pillar_1.9.0 norm_1.0-11.1 lifecycle_1.0.3
## [109] BiocManager_1.30.22 jquerylib_0.1.4 MALDIquant_1.22.1
## [112] GlobalOptions_0.1.2 data.table_1.14.8 cowplot_1.1.1
## [115] bitops_1.0-7 httpuv_1.6.11 R6_2.5.1
## [118] pcaMethods_1.90.0 affy_1.76.0 promises_1.2.1
## [121] KernSmooth_2.23-22 codetools_0.2-19 MASS_7.3-60
## [124] gtools_3.9.4 assertthat_0.2.1 chron_2.3-61
## [127] proto_1.0.0 rjson_0.2.21 withr_2.5.0
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3 hms_1.1.3
## [133] grid_4.2.3 rmarkdown_2.23 shiny_1.7.4.1knitr::knit_exit()